Chapter 5 Community composition
5.1 Taxonomy overview
5.1.1 Stacked barplot
genome_counts_filt_met<-genome_counts_filt %>%
mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
left_join(., sample_metadata, by = join_by(sample == EHI_number)) %>% #append sample metadata
filter(count > 0) #filter 0 counts
genome_counts_filt_met$Elevation<-as.factor(genome_counts_filt_met$Elevation)
# Create an interaction variable for elevation and sample
genome_counts_filt_met$interaction_var <- interaction(genome_counts_filt_met$sample, genome_counts_filt_met$Elevation)
# Plot stacked barplot
ggplot(genome_counts_filt_met, aes(x=interaction_var,y=count,fill=phylum, group=phylum))+ #grouping enables keeping the same sorting of taxonomic units
geom_bar(stat="identity", colour="white", linewidth=0.1)+ #plot stacked bars with white borders
scale_fill_manual(values=phylum_colors) +
labs(y = "Relative abundance", x="Elevation (m)") +
guides(fill = guide_legend(ncol = 3)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
panel.background = element_blank(),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(linewidth = 0.5, linetype = "solid", colour = "black"),
legend.position = "top",
legend.title = element_blank(),
legend.text = element_text(size=7))+
scale_x_discrete(labels = function(x) gsub(".*\\.", "", x)) +
facet_wrap(~Transect, scales = "free", labeller = as_labeller(function(label) gsub(".*\\.", "", label))) #only show elevation label5.1.2 Phylum relative abundances
phylum_summary <- genome_counts_filt %>%
mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
pivot_longer(-genome, names_to = "sample", values_to = "count") %>%
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
left_join(genome_metadata, by = join_by(genome == genome)) %>%
group_by(sample,phylum) %>%
summarise(relabun=sum(count))
phylum_summary %>%
group_by(phylum) %>%
summarise(mean=mean(relabun),sd=sd(relabun)) %>%
arrange(-mean) %>%
tt()| phylum | mean | sd |
|---|---|---|
| p__Bacillota_A | 4.081110e-01 | 0.1600451966 |
| p__Bacteroidota | 3.813465e-01 | 0.1589155381 |
| p__Pseudomonadota | 5.849246e-02 | 0.0783694483 |
| p__Bacillota | 4.549049e-02 | 0.0635314708 |
| p__Campylobacterota | 3.481511e-02 | 0.0560037828 |
| p__Desulfobacterota | 2.666332e-02 | 0.0402945087 |
| p__Verrucomicrobiota | 1.518957e-02 | 0.0242135928 |
| p__Bacillota_C | 9.477405e-03 | 0.0104659743 |
| p__Fusobacteriota | 8.967841e-03 | 0.0368793328 |
| p__Cyanobacteriota | 3.965422e-03 | 0.0059815655 |
| p__Spirochaetota | 2.170591e-03 | 0.0096628708 |
| p__Bacillota_B | 2.069011e-03 | 0.0034571169 |
| p__Actinomycetota | 1.681425e-03 | 0.0051589130 |
| p__Chlamydiota | 1.027149e-03 | 0.0060969825 |
| p__Deferribacterota | 4.549927e-04 | 0.0026507460 |
| p__Synergistota | 7.766526e-05 | 0.0007996128 |
phylum_arrange <- phylum_summary %>%
group_by(phylum) %>%
summarise(mean=mean(relabun)) %>%
arrange(-mean) %>%
select(phylum) %>%
pull()
phylum_summary %>%
filter(phylum %in% phylum_arrange) %>%
mutate(phylum=factor(phylum,levels=rev(phylum_arrange))) %>%
ggplot(aes(x=relabun, y=phylum, group=phylum, color=phylum)) +
scale_color_manual(values=phylum_colors[rev(phylum_arrange)]) +
geom_jitter(alpha=0.5) +
theme_minimal()5.2 Taxonomy boxplot
5.2.1 Family
family_summary <- genome_counts_filt %>%
mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>% #append sample metadata
left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
group_by(sample,family) %>%
summarise(relabun=sum(count))
family_summary %>%
group_by(family) %>%
summarise(mean=mean(relabun),sd=sd(relabun)) %>%
arrange(-mean) %>%
tt()| family | mean | sd |
|---|---|---|
| f__Lachnospiraceae | 2.825060e-01 | 0.1345643318 |
| f__Bacteroidaceae | 2.119504e-01 | 0.1049201320 |
| f__Tannerellaceae | 1.118797e-01 | 0.0753626317 |
| f__ | 6.502966e-02 | 0.0818702640 |
| f__Helicobacteraceae | 3.481511e-02 | 0.0560037828 |
| f__Marinifilaceae | 3.470570e-02 | 0.0255772494 |
| f__UBA3700 | 2.788514e-02 | 0.0328903056 |
| f__Desulfovibrionaceae | 2.666332e-02 | 0.0402945087 |
| f__Ruminococcaceae | 2.425877e-02 | 0.0226024661 |
| f__Rikenellaceae | 1.885691e-02 | 0.0176408335 |
| f__Erysipelotrichaceae | 1.751136e-02 | 0.0268864962 |
| f__Oscillospiraceae | 1.683003e-02 | 0.0141001689 |
| f__Coprobacillaceae | 1.261022e-02 | 0.0335546952 |
| f__Mycoplasmoidaceae | 1.198842e-02 | 0.0269229159 |
| f__Enterobacteriaceae | 1.066950e-02 | 0.0648238061 |
| f__Fusobacteriaceae | 8.967841e-03 | 0.0368793328 |
| f__Akkermansiaceae | 8.595505e-03 | 0.0108643967 |
| f__CAG-239 | 6.937215e-03 | 0.0097788795 |
| f__LL51 | 6.594063e-03 | 0.0215416732 |
| f__Anaerotignaceae | 6.489923e-03 | 0.0073982451 |
| f__UBA3830 | 5.877671e-03 | 0.0184934716 |
| f__Gastranaerophilaceae | 3.965422e-03 | 0.0059815655 |
| f__Muribaculaceae | 3.953836e-03 | 0.0407072381 |
| f__Butyricicoccaceae | 3.918292e-03 | 0.0050279924 |
| f__CAG-274 | 3.022828e-03 | 0.0060610627 |
| f__Acutalibacteraceae | 2.697920e-03 | 0.0047318531 |
| f__Anaerovoracaceae | 2.658130e-03 | 0.0040447826 |
| f__Pumilibacteraceae | 2.520520e-03 | 0.0041575482 |
| f__UBA1997 | 2.361296e-03 | 0.0079920448 |
| f__CAG-508 | 2.302152e-03 | 0.0062849154 |
| f__Brevinemataceae | 2.170591e-03 | 0.0096628708 |
| f__Peptococcaceae | 2.069011e-03 | 0.0034571169 |
| f__Rhodocyclaceae | 1.918345e-03 | 0.0193815077 |
| f__DTU072 | 1.775970e-03 | 0.0055679110 |
| f__UBA660 | 1.726020e-03 | 0.0054429829 |
| f__MGBC116941 | 1.698183e-03 | 0.0090561944 |
| f__Massilibacillaceae | 1.488714e-03 | 0.0024454858 |
| f__Eggerthellaceae | 1.364529e-03 | 0.0037021644 |
| f__Enterococcaceae | 8.375225e-04 | 0.0070514701 |
| f__CALTSX01 | 5.149633e-04 | 0.0053018713 |
| f__Chlamydiaceae | 5.121857e-04 | 0.0030351423 |
| f__CALVMC01 | 4.985785e-04 | 0.0019169750 |
| f__Mucispirillaceae | 4.549927e-04 | 0.0026507460 |
| f__Clostridiaceae | 4.173001e-04 | 0.0028996742 |
| f__Acidaminococcaceae | 3.966660e-04 | 0.0015706727 |
| f__UBA1242 | 3.684261e-04 | 0.0014638710 |
| f__RUG11792 | 3.682076e-04 | 0.0019335896 |
| f__CAG-465 | 3.464214e-04 | 0.0015624302 |
| f__Microbacteriaceae | 3.168954e-04 | 0.0026872384 |
| f__CAG-288 | 2.957044e-04 | 0.0017433720 |
| f__Streptococcaceae | 2.456082e-04 | 0.0018352415 |
| f__Anaplasmataceae | 2.412140e-04 | 0.0021407333 |
| f__Hepatoplasmataceae | 2.339936e-04 | 0.0024091117 |
| f__Aeromonadaceae | 2.305128e-04 | 0.0022380802 |
| f__Peptostreptococcaceae | 1.398982e-04 | 0.0014403401 |
| f__Rhodobacteraceae | 1.358917e-04 | 0.0013990908 |
| f__Xanthomonadaceae | 9.158653e-05 | 0.0009429410 |
| f__Synergistaceae | 7.766526e-05 | 0.0007996128 |
| f__Lactobacillaceae | 4.163513e-05 | 0.0004286599 |
| f__Turicibacteraceae | 0.000000e+00 | 0.0000000000 |
5.2.2 Genus
genus_summary <- genome_counts_filt %>%
mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>% #append sample metadata
left_join(genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
group_by(sample,genus) %>%
summarise(relabun=sum(count)) %>%
filter(genus != "g__")
genus_summary %>%
group_by(genus) %>%
summarise(mean=mean(relabun),sd=sd(relabun)) %>%
arrange(-mean) %>%
tt()| genus | mean | sd |
|---|---|---|
| g__Bacteroides | 1.715631e-01 | 0.0995381723 |
| g__Parabacteroides | 1.099480e-01 | 0.0764703201 |
| g__Roseburia | 5.106281e-02 | 0.0740647328 |
| g__Phocaeicola | 3.848609e-02 | 0.0434625541 |
| g__JAAYNV01 | 3.688241e-02 | 0.0750152647 |
| g__Odoribacter | 3.406510e-02 | 0.0255496567 |
| g__Helicobacter_J | 2.995383e-02 | 0.0398840003 |
| g__CAG-95 | 1.693584e-02 | 0.0241924899 |
| g__Alistipes | 1.544141e-02 | 0.0156462125 |
| g__Kineothrix | 1.529781e-02 | 0.0582217172 |
| g__MGBC136627 | 1.442882e-02 | 0.0229993736 |
| g__Mycoplasmoides | 1.128813e-02 | 0.0267998413 |
| g__Hungatella_A | 1.103483e-02 | 0.0667132423 |
| g__Anaerotruncus | 1.001128e-02 | 0.0112222704 |
| g__Velocimicrobium | 9.877812e-03 | 0.0159828933 |
| g__Enterocloster | 9.448113e-03 | 0.0098167225 |
| g__Acetatifactor | 9.119894e-03 | 0.0139402425 |
| g__Fusobacterium_A | 8.967841e-03 | 0.0368793328 |
| g__Akkermansia | 8.595505e-03 | 0.0108643967 |
| g__Clostridium_Q | 7.696833e-03 | 0.0137729617 |
| g__Bilophila | 7.309730e-03 | 0.0088486876 |
| g__Lawsonia | 7.063171e-03 | 0.0354503009 |
| g__Intestinimonas | 6.503115e-03 | 0.0061322665 |
| g__Lacrimispora | 6.449964e-03 | 0.0078252870 |
| g__Lachnotalea | 5.969286e-03 | 0.0091428683 |
| g__Desulfovibrio | 5.551727e-03 | 0.0084902145 |
| g__MGBC140009 | 5.480884e-03 | 0.0133474239 |
| g__Extibacter | 5.251389e-03 | 0.0436025301 |
| g__Coprobacillus | 5.206125e-03 | 0.0159496122 |
| g__Eisenbergiella | 5.044262e-03 | 0.0077976628 |
| g__Ventrimonas | 5.035349e-03 | 0.0082923216 |
| g__NHYM01 | 4.861275e-03 | 0.0425676661 |
| g__Dielma | 4.818065e-03 | 0.0065339333 |
| g__CHH4-2 | 4.421332e-03 | 0.0044698946 |
| g__RGIG4733 | 4.212224e-03 | 0.0102969273 |
| g__Negativibacillus | 4.095555e-03 | 0.0054792324 |
| g__Thomasclavelia | 3.821798e-03 | 0.0117039604 |
| g__Hungatella | 3.408253e-03 | 0.0046301753 |
| g__C-19 | 3.303166e-03 | 0.0097049178 |
| g__Citrobacter | 3.169481e-03 | 0.0206845556 |
| g__UMGS1251 | 2.856625e-03 | 0.0066227314 |
| g__Oscillibacter | 2.672493e-03 | 0.0038359772 |
| g__CAZU01 | 2.662464e-03 | 0.0062677129 |
| g__Copromonas | 2.582309e-03 | 0.0037444602 |
| g__Breznakia | 2.545152e-03 | 0.0076365238 |
| g__Mailhella | 2.463379e-03 | 0.0034270857 |
| g__Pseudoflavonifractor | 2.327661e-03 | 0.0028208232 |
| g__Intestinibacillus | 2.304475e-03 | 0.0027604605 |
| g__Escherichia | 2.211570e-03 | 0.0159347159 |
| g__MGBC165282 | 2.207931e-03 | 0.0052013921 |
| g__Brevinema | 2.170591e-03 | 0.0096628708 |
| g__Rikenella | 2.103581e-03 | 0.0033511480 |
| g__Morganella | 2.000896e-03 | 0.0206004803 |
| g__Robinsoniella | 1.982065e-03 | 0.0197231357 |
| g__Parabacteroides_B | 1.931687e-03 | 0.0064193758 |
| g__Hafnia | 1.921419e-03 | 0.0111820580 |
| g__Fluviibacter | 1.918345e-03 | 0.0193815077 |
| g__JAIHAL01 | 1.891324e-03 | 0.0043767561 |
| g__CAJLXD01 | 1.792508e-03 | 0.0041225034 |
| g__Marseille-P3106 | 1.713488e-03 | 0.0025261999 |
| g__UBA866 | 1.521215e-03 | 0.0026067620 |
| g__MGBC116941 | 1.419756e-03 | 0.0090572569 |
| g__Duncaniella | 1.413153e-03 | 0.0145492957 |
| g__RGIG6463 | 1.406224e-03 | 0.0032145982 |
| g__Stoquefichus | 1.405377e-03 | 0.0045901612 |
| g__Limenecus | 1.380444e-03 | 0.0029775228 |
| g__JAAYQI01 | 1.259608e-03 | 0.0019959178 |
| g__Lawsonibacter | 1.166300e-03 | 0.0016508459 |
| g__Scatousia | 1.163255e-03 | 0.0033190366 |
| g__MGBC101980 | 1.136457e-03 | 0.0043661490 |
| g__Hespellia | 1.092457e-03 | 0.0079658275 |
| g__Clostridium_AQ | 1.065062e-03 | 0.0036811111 |
| g__Tidjanibacter | 1.052202e-03 | 0.0029270036 |
| g__Fournierella | 1.012242e-03 | 0.0022501066 |
| g__Eggerthella | 9.852582e-04 | 0.0031664138 |
| g__OM05-12 | 9.476666e-04 | 0.0022861996 |
| g__CALXRO01 | 9.465351e-04 | 0.0060115481 |
| g__CALURL01 | 9.003034e-04 | 0.0021568633 |
| g__Harryflintia | 8.821994e-04 | 0.0020357355 |
| g__MGBC133411 | 8.788515e-04 | 0.0021894657 |
| g__Scatacola_A | 8.603252e-04 | 0.0028008223 |
| g__Ventrisoma | 8.513312e-04 | 0.0015698585 |
| g__JALFVM01 | 8.313744e-04 | 0.0020032515 |
| g__Bacteroides_G | 8.279547e-04 | 0.0024893653 |
| g__CAG-269 | 8.108577e-04 | 0.0029383065 |
| g__IOR16 | 8.056128e-04 | 0.0024044902 |
| g__CAG-873 | 7.720491e-04 | 0.0079487323 |
| g__Buttiauxella | 7.491009e-04 | 0.0061893836 |
| g__14-2 | 7.174613e-04 | 0.0014179404 |
| g__Ureaplasma | 7.002851e-04 | 0.0022713191 |
| g__Scatocola | 6.837988e-04 | 0.0024038571 |
| g__Dysosmobacter | 6.815887e-04 | 0.0012774053 |
| g__Muricomes | 6.778722e-04 | 0.0023619925 |
| g__Anaerovorax | 6.716036e-04 | 0.0017450208 |
| g__UBA7185 | 6.627870e-04 | 0.0018432854 |
| g__Evtepia | 6.474991e-04 | 0.0010541613 |
| g__Butyricimonas | 6.406058e-04 | 0.0016068308 |
| g__MGBC131033 | 6.382237e-04 | 0.0015934066 |
| g__CAJMNU01 | 6.222337e-04 | 0.0008887008 |
| g__Beduini | 5.858796e-04 | 0.0013098648 |
| g__Muribaculum | 5.497694e-04 | 0.0056602225 |
| g__Scandinavium | 5.341634e-04 | 0.0034686403 |
| g__Lactonifactor | 5.289757e-04 | 0.0013117146 |
| g__CALTSX01 | 5.149633e-04 | 0.0053018713 |
| g__CAG-485 | 5.079634e-04 | 0.0052298033 |
| g__Merdicola | 5.061702e-04 | 0.0018103952 |
| g__Ventrenecus | 4.907338e-04 | 0.0024331147 |
| g__UMGS1202 | 4.839872e-04 | 0.0016896720 |
| g__Copranaerobaculum | 4.782162e-04 | 0.0029031775 |
| g__NSJ-61 | 4.531589e-04 | 0.0013984436 |
| g__Faecimonas | 4.425060e-04 | 0.0017543086 |
| g__RGIG8482 | 4.373523e-04 | 0.0020895489 |
| g__Faecivivens | 4.272845e-04 | 0.0008894505 |
| g__RGIG9287 | 4.188304e-04 | 0.0020906022 |
| g__Sarcina | 4.173001e-04 | 0.0028996742 |
| g__Blautia_A | 4.105640e-04 | 0.0009994663 |
| g__Scatenecus | 4.064313e-04 | 0.0026411296 |
| g__Phascolarctobacterium | 3.966660e-04 | 0.0015706727 |
| g__Raoultibacter | 3.792710e-04 | 0.0011559737 |
| g__Caccovivens | 3.684261e-04 | 0.0014638710 |
| g__CAJTFG01 | 3.655350e-04 | 0.0010180529 |
| g__HGM11386 | 3.608509e-04 | 0.0015988134 |
| g__CAG-465 | 3.464214e-04 | 0.0015624302 |
| g__Amedibacillus | 3.424477e-04 | 0.0023723526 |
| g__Enterococcus_A | 3.245757e-04 | 0.0023143225 |
| g__UMGS2016 | 3.182000e-04 | 0.0012854921 |
| g__Emergencia | 3.175682e-04 | 0.0009661193 |
| g__Holdemania | 3.069052e-04 | 0.0010242364 |
| g__Blautia | 3.066198e-04 | 0.0011028540 |
| g__Protoclostridium | 3.038491e-04 | 0.0010789526 |
| g__Fimivivens | 3.014823e-04 | 0.0008070986 |
| g__RGIG7389 | 2.957300e-04 | 0.0005828033 |
| g__CAG-345 | 2.957044e-04 | 0.0017433720 |
| g__UBA7173 | 2.935763e-04 | 0.0030225525 |
| g__Bariatricus | 2.900841e-04 | 0.0008344380 |
| g__Agathobaculum | 2.760992e-04 | 0.0016313981 |
| g__CALXDZ01 | 2.635870e-04 | 0.0006108419 |
| g__UBA940 | 2.597096e-04 | 0.0009035857 |
| g__Microbacterium | 2.538897e-04 | 0.0026139544 |
| g__Aminipila | 2.482408e-04 | 0.0007447481 |
| g__Lactococcus | 2.456082e-04 | 0.0018352415 |
| g__Wolbachia | 2.412140e-04 | 0.0021407333 |
| g__Paramuribaculum | 2.409489e-04 | 0.0024807206 |
| g__Hepatoplasma | 2.339936e-04 | 0.0024091117 |
| g__Aeromonas | 2.305128e-04 | 0.0022380802 |
| g__WRHT01 | 2.165572e-04 | 0.0006925426 |
| g__Zhenpiania | 2.096592e-04 | 0.0012649339 |
| g__UBA5026 | 2.092951e-04 | 0.0009734373 |
| g__UMGS1663 | 1.980727e-04 | 0.0007253856 |
| g__MGBC107952 | 1.736116e-04 | 0.0009841510 |
| g__CALXEL01 | 1.709372e-04 | 0.0013664186 |
| g__CAG-273 | 1.468577e-04 | 0.0007828475 |
| g__Clostridioides | 1.398982e-04 | 0.0014403401 |
| g__Paracoccus | 1.358917e-04 | 0.0013990908 |
| g__JAAWBF01 | 1.274842e-04 | 0.0006116591 |
| g__JAFLTL01 | 1.258715e-04 | 0.0012959262 |
| g__Bacteroides_H | 1.255258e-04 | 0.0012923672 |
| g__RUG12867 | 9.167312e-05 | 0.0006100737 |
| g__Stenotrophomonas | 9.158653e-05 | 0.0009429410 |
| g__Rahnella | 8.286698e-05 | 0.0008021325 |
| g__Lumbricidophila | 6.300569e-05 | 0.0006486833 |
| g__UBA3263 | 5.050541e-05 | 0.0005199850 |
| g__Fructobacillus | 4.163513e-05 | 0.0004286599 |
| g__Clostridium | 0.000000e+00 | 0.0000000000 |
| g__Turicibacter | 0.000000e+00 | 0.0000000000 |
5.3 Alpha diversity
# Calculate Hill numbers
richness <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 0) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(richness = 1) %>%
rownames_to_column(var = "sample")
neutral <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 1) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(neutral = 1) %>%
rownames_to_column(var = "sample")
phylogenetic <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 1, tree = genome_tree) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(phylogenetic = 1) %>%
rownames_to_column(var = "sample")
# Aggregate basal GIFT into elements (error in this section)
#Get list of present MAGs
present_MAGs <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
filter(rowSums(.[, -1]) != 0) %>%
rownames()
#Align KEGG annotations with present MAGs and remove all-zero and all-one traits
present_MAGs <- present_MAGs[present_MAGs %in% rownames(genome_gifts)]
genome_gifts_filt <- genome_gifts[present_MAGs,] %>%
select_if(~!all(. == 0)) %>% #remove all-zero modules
select_if(~!all(. == 1)) #remove all-one modules
#Filter count table to only contain present MAGs after KEGG filtering
genome_counts_filt_filt <- genome_counts_filt %>%
column_to_rownames(var = "genome")
genome_counts_filt_filt <- genome_counts_filt_filt[present_MAGs,]
dist <- genome_gifts_filt %>%
to.elements(., GIFT_db) %>%
traits2dist(., method = "gower")
functional <- genome_counts_filt_filt %>%
#column_to_rownames(var = "genome") %>%
#dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 1, dist = dist) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(functional = 1) %>%
rownames_to_column(var = "sample") %>%
mutate(functional = if_else(is.nan(functional), 1, functional))
# Merge all metrics
alpha_div <- richness %>%
full_join(neutral, by = join_by(sample == sample)) %>%
full_join(phylogenetic, by = join_by(sample == sample)) %>%
full_join(functional, by = join_by(sample == sample))alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == EHI_number)) %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = Transect, group=Transect, color=Transect, fill=Transect)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Transect",
breaks=c("Aisa","Aran","Sentein","Tourmalet"),
labels=c("Aisa","Aran","Sentein","Tourmalet"),
values=c("#e5bd5b", "#6b7398","#e2815a", "#876b96")) +
scale_fill_manual(name="Transect",
breaks=c("Aisa","Aran","Sentein","Tourmalet"),
labels=c("Aisa","Aran","Sentein","Tourmalet"),
values=c("#e5bd5b50", "#6b739850","#e2815a50", "#876b9650")) +
facet_wrap(. ~ metric, scales = "free", ncol=4) +
coord_cartesian(xlim = c(1, NA)) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
)5.3.1 Regression plots
5.3.1.1 Richness diversity
columns <- c("richness","neutral","phylo","func","mapped","total")
alpha_div %>%
select(sample,richness) %>%
pivot_longer(-sample, names_to = "data", values_to = "value") %>%
mutate(data = factor(data, levels = columns)) %>%
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
ggplot(aes(x = Elevation, y = value)) +
geom_point() +
geom_smooth(method = lm) +
facet_wrap(~ factor(Transect))+
labs(x = "Elevation (m)")5.3.1.2 Neutral diversity
alpha_div %>%
select(sample,neutral) %>%
pivot_longer(-sample, names_to = "data", values_to = "value") %>%
mutate(data = factor(data, levels = columns)) %>%
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
ggplot(aes(x = Elevation, y = value)) +
geom_point() +
geom_smooth(method = lm) +
facet_wrap(~ factor(Transect))+
labs(x = "Elevation (m)")5.3.1.3 Phylogenetic diversity
alpha_div %>%
select(sample,phylogenetic) %>%
pivot_longer(-sample, names_to = "data", values_to = "value") %>%
mutate(data = factor(data, levels = columns)) %>%
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
ggplot(aes(x = Elevation, y = value)) +
geom_point() +
geom_smooth(method = lm) +
facet_wrap(~ factor(Transect))+
labs(x = "Elevation (m)")5.3.1.4 Functional diversities
alpha_div %>%
select(sample,functional) %>%
pivot_longer(-sample, names_to = "data", values_to = "value") %>%
mutate(data = factor(data, levels = columns)) %>%
left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
ggplot(aes(x = Elevation, y = value)) +
geom_point() +
geom_smooth(method = lm) +
facet_wrap(~ factor(Transect))+
labs(x = "Elevation (m)")5.4 Beta diversity
beta_q0n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
hillpair(., q = 0)
beta_q1n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
hillpair(., q = 1)
beta_q1p <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
hillpair(., q = 1, tree = genome_tree)
beta_q1f <- genome_counts_filt_filt %>%
#column_to_rownames(., "genome") %>%
hillpair(., q = 1, dist = dist)